Cardiopulmonary and metabolic responses during a 2-day CPET in [ME/CFS]: translating reduced oxygen consumption [...], Keller et al, 2024

Discussion in 'ME/CFS research' started by Nightsong, Jul 5, 2024.

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  1. forestglip

    forestglip Senior Member (Voting Rights)

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    Let's see if this holds going the other way. Instead of percent change from day 1 to day 2 based on day 1, I got the percent change from day 2 to day 1 based on day 2:
    d2Wkld_wkldDiffPctRvrsd.png


    Yes, so pretending day 2 is day 1, ME/CFS will have the super high outliers going in reverse too, because they have the lowest AT on day 2 as well. In this case, there's a single blue with the nine reds in the >100% outlier group.

    Edit: This only affects the percentage change that we are looking at. All the studies have focused on absolute change, which has the more obvious reason ME/CFS doesn't seem to decrease as much that I mentioned. There's not as far down to go.

    d1Wkld_wkldDiffAbs.png
    And would you look at that. The higher their day 1 workload, the greater their decrease. And the blues make up the highest spots.

    Pearson correlation is -0.38 with a p-value of 1.5e-6. Spearman is -0.34 with p-value of 2.2e-5.

    Edit: Significant for both groups by themselves as well:
    HC: PearsonRResult(statistic=-0.47726530494758557, pvalue=2.573080560882015e-05)
    MECFS: PearsonRResult(statistic=-0.39724390980517693, pvalue=0.00020047296439091046)

    ---

    So it looks to me like people with ME/CFS have a nearly significant difference for workload change, not because they are more deconditioned, but in spite of being more deconditioned.

    Edit: Also, that chart above matches all this surprisingly well. Regression lines for each group are parallel, with the ME/CFS line about 10 watts lower than HC, which suggests that if you control for day 1 workload, ME will decrease 10 watts more (on average).
     
    Last edited: Sep 17, 2024
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  2. forestglip

    forestglip Senior Member (Voting Rights)

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    So why does ME/CFS have a lower workload at AT? Is it deconditioning? Do the groups need to be better matched for deconditioning, not to show if the effect still exists, but instead to make the effect clearer?

    I tried looking to see if there was a correlation using means from all studies between day 1 AT workload and absolute workload change.
    allStudies_d1AtWkld_AtWkldDiffAbs.png
    Not really that I can see for everyone combined. I was expecting all the lines to maybe look like the red line. Controls seem to be doing the opposite of my hypothesis (though not significant, unlike for individuals in Keller where it is significant and going down.) Percentage chart looks pretty similar to this absolute chart.
     
    Last edited: Sep 17, 2024
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  3. ME/CFS Skeptic

    ME/CFS Skeptic Senior Member (Voting Rights)

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    Conceptually I find it difficult to see why a low value on day1 would make it easier to have a large percentage increase.

    The way I see it each participant has a hypothetical mean, the average value they would get if they were tested infinite times. There will be some variation around that mean but this should be the same for everyone, regardless of ow big your average value is in absolute terms. So it shouldn't cause differences between ME/CFS patients and controls.

    If you take the lowest values on day1, then I assume chances are higher that they are below their own personal average value than for someone in the middle or higher end. But this should be the same in both groups.

    If you were to remove the 4 outliers from this graph, I don't think it would look that much different for both groups, except that HC have a higher mean for day1 values.
     
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  4. forestglip

    forestglip Senior Member (Voting Rights)

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    Here's what I'm thinking.

    First, if you remove the outliers, the percentage chart still looks like it flares out going down towards lower day one workloads. The relationship still seems to exist that you'll see more extreme changes at lower workloads.

    That said, I don't see why they should be removed. They look like they fit very well with the rest of the points into the "flaring out" distribution. It doesn't look like anything is wildly abnormal in this graph about these four results specifically.

    Second, the why isn't as important as the what. What we're seeing is that there's either something unique about ME/CFS or about people with low day one workloads that allows them to have very large increases. Seeing as the four outliers are only found in the lowest zone of day one workloads, I think it's the latter. I think if we saw more controls with sub-30 workloads, we'd see more controls as outliers.

    Speculation on the why:

    Option 1: Maybe "percent change" doesn't really work with this for some reason. Maybe variation on CPETs is more "absolute". Whether you can achieve a workload of 50 or 150, your personal random variation will be ±50 watts, not ±50%.

    We see that with the absolute changes chart, it looks a lot more "behaved". No outliers, just what appears to be a somewhat strong linear relationship.

    Option 2: Maybe lower results on day one workload correlate to muscles which have a harder time maintaining a constant pedal rate, so we'd see bigger swings. Maybe weak muscles explain both of those.
     
  5. forestglip

    forestglip Senior Member (Voting Rights)

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    I totally wasted too much time on this, but I found out you can make animations in MatPlotLib, so I had to try this. I animated that graph of day 1 workload vs change in workload from a few posts up and show how it changes if you normalize the change value based on the day 1 value. Basically making the regression lines horizontal.

    I didn't even write any of the code myself, it was pretty much 100% AI, but it still took over 5 hours of telling it to tweak things to get it to work correctly.

    It's a GIF that might be kind of a large file size (~2 MB) for people with slow internet, so click the thumbnail if you want to view it.
    scatter_transition_with_regression_skew_and_final_swarm_loop-ezgif.com-resize(2).gif

    Anyway, the important part, the statistics, took a few minutes.

    How I normalized the values: First I got the slopes of the two regression lines for ME/CFS and HC from the plot above of D1_AT_wkld vs AT_wkld_diff_absolute. Since the lines for the groups are independently calculated, and yet pretty much parallel, I think there's a good chance they represent close to the true real world slope of how day 1 workload affects how much your workload will change on the next test, at least for fairly sedentary people, which both groups were. Since they're not exactly the same slope, I got the average of the two, which came out to about -0.3274.

    To normalize the change value, I subtracted (-0.3274*D1_wkld) from the change. This makes it so that the regression lines are just about horizontal, so that individuals with different day 1 workloads can now be compared since they aren't sloping towards greater decreases if they start with higher values, which healthy controls were.

    Here's the original and the normalized data with the regression lines, same as in the GIF:

    AT_wkld_diff_absolute_regression.png normalized_AT_wkld_diff_absolute_regression.png

    And here are the groups side by side before and after normalization:

    AT_wkld_diff_absolute_swarm.png normalized_AT_wkld_diff_absolute_swarm.png

    And now the statistical tests. Using normalized change, I get p values less than 0.001. Cohen's d effect size is 0.535.

    Again the equation for normalization is:
    Code:
    normalized_AT_wkld_diff_absolute = AT_wkld_diff_absolute - (-0.3274*D1_AT_wkld)
    And that -0.3274 number is just the slope of the line halfway between the blue and red line above.

    upload_2024-9-17_20-36-5.png

    To try to explain it in simpler terms, there appears to be a trend where the higher the workload on day one of these participants, the more they decreased on day two. The same trend in both groups: for every one watt higher they worked on day one before hitting anaerobic threshold, they, on average, decreased about 0.33 watts more on the next day.

    So now imagine you have a person with ME/CFS that gets 50 watts on day one and decreases by 5 points the next day, and a healthy person that gets 70 watts on day one and decreases by 7 points. It looks like the healthy person decreased more than the person with ME.

    But wait. The healthy person, just on account of the trend we saw previously, is expected to decrease 0.33 more for every watt higher they started than the other participant. So they started 70 - 50 = 20 watts higher on day one. 20 * 0.33 = 6.6 watts. They would be expected to decrease 6.6 watts more than the other person. But they only decreased 2 watts more. Which suggests that either the healthy person isn't decreasing as much as you would expect, or the person with ME/CFS is decreasing more.

    What the normalization equation above is doing is simply subtracting the extra "change" watts that participants are expected to decrease just from starting out higher. Put them on level footing so we can compare the difference in watts changed that is actually interesting. So with the example of the two people above, you would subtract 0.33 times the number of watts they started at from how much they changed:

    ME: 50*0.33 watts subtracted from 5 = increase of 11.5 watts.
    HC: 70*0.33 watts subtracted from 7 = increase of 16.1 watts

    The actual increase values by themselves aren't important, it's comparing between them. We could subtract both by a constant like 16.1 watts if we assume the healthy person's value is normal to get the numbers looking prettier.

    Anyway, what we see is that if we don't count the extra watts a person will decrease just from starting higher, the example person with ME decreased 4.6 watts more (or increased 4.6 watts less) than expected for some reason.

    So that's basically what I see in the real study data above. When I take away those extra free "change watts" from every participant, which is what's happening with skewing the graphs, the ME participants decreased 9 watts more than the controls on average. (You can see the means of the two groups using normalized data in the statistical test screenshot above.)

    Of course, there might not be a real correlation. Or there is, but the real slope of the real world effect is different from what the slope of the study's data looks like, which is about 0.33, and what I'm basing this on.

    I only think it's a real effect, as well as close to 0.33, because both groups individually have that same slope (or very near it), and the correlation is significant in both groups.

    So the upshot is that I think, based on these results, if you compared two groups with similar workloads on day one, you would see a significantly greater decrease in the ME group. Or if you used this normalization equation for dissimilar groups, you would still see a greater decrease in the ME group.

    ----

    Edit: Slopes and confidence intervals for the two groups:
    ME/CFS: -0.338, 95% CI [-0.510, -0.165]
    Controls: -0.317, 95% CI [-0.457, -0.177]
     
    Last edited: Sep 18, 2024
  6. ME/CFS Skeptic

    ME/CFS Skeptic Senior Member (Voting Rights)

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  7. ME/CFS Skeptic

    ME/CFS Skeptic Senior Member (Voting Rights)

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    Conclusion
    In conclusion, the largest and highest quality study on 2-day exercise testing did not find strong evidence of impaired recovery in ME/CFS patients. This suggests that the effects are smaller than initially thought and that the procedure has difficulty in accurately differentiating patients and controls. The data, however, are consistent with a small to moderate effect for VO2 Peak and perhaps for Workload AT as well, depending on how you analyze the data and handle outliers.

    In a future blog post, we will take a closer look at previous studies of 2-day exercise testing in ME/CFS patients.

    Acknowledgments
    Many thanks to forestglip on the Science for ME forum whose thoughtful analysis was very helpful in understanding the Keller et al. dataset.

    The following dataset was used from www.mapmecfs.org: Keller et al. Cardiopulmonary and metabolic responses during a 2-day CPET in ME/CFS. Last updated: September 12, 2024, 4:29 PM (UTC+02:00).
     
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  8. forestglip

    forestglip Senior Member (Voting Rights)

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  9. forestglip

    forestglip Senior Member (Voting Rights)

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    I made a first attempt at making a workload at AT matched dataset, as opposed to the study's VO2 max matched dataset. I don't know if there's some specific algorithm for doing this task, but an AI's idea was a NearestNeighbors algorithm to find matching pairs at close distances based on sex, bmi, age, and day one workload at AT. It does work, and the pairs look pretty good, I just think a better implementation could get a larger dataset than this crude version. I only got 21 pairs vs the study's 55 pairs. Example pairings of the first few rows:

    upload_2024-9-18_22-24-57.png

    And here are the workloads in the new matched set:

    D1_AT_wkld_swarm.png

    And absolute and percentage differences:
    AT_wkld_diff_absolute_swarm.png AT_wkld_diff_percentage_swarm.png

    But anyway, even with only 21 ME/CFS and 21 controls, but now matched for day one workload, the two day difference in workload at AT, both absolute and percent, is significant:

    upload_2024-9-18_22-28-41.png

    Getting fairly small p-values (~0.01) and pretty large effect sizes (~0.7).

    I'll add the code I used to make the dataset as an attachment to verify I didn't hand select rows to make this work. Though note I did first try this with larger thresholds (distance that two participants' values can be from each other) first, and the p-values were larger (around 0.5 to 0.7). The above data is from a threshold of 0.5.
    (The only participant excluded in the input csv is PI-026.)

    I also tried matching only based on day one workload, and not caring about BMI or age (in the attached code, I removed "bmi" and "age" from "matching features" and lowered threshold to 0.3 to get even closer matching), and the p-values are even lower (<0.01 for percentage):
    upload_2024-9-18_22-50-32.png

    Day one workload and difference between days in this larger dataset:
    D1_AT_wkld_swarm.png AT_wkld_diff_percentage_swarm.png

    Edit: I changed the attached script so that you can just use the raw datafile from mapmecfs as input. Just put the script in the same directory as the dataset, or change the path in the script, and after running, it will create an identical TSV but with a new column called new_matched_pair. Should be identical participants to the ones in this post. Threshold and columns to match on can be changed if desired.
     

    Attached Files:

    Last edited: Sep 19, 2024
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  10. ME/CFS Skeptic

    ME/CFS Skeptic Senior Member (Voting Rights)

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    I don't know either. I was wondering how they did this - perhaps they recruited controls with the intention to match patients. Anyway, interesting analysis.

    Ok thanks, I've posted it here:
    https://www.s4me.info/threads/the-biggest-2-day-exercise-study-blog-me-cfs-skeptic.40267/
     
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